Because of recent advances in computing technology and the availability of large datasets, deep learning has risen to the forefront of artificial intelligence, with performances that often equal, or sometimes even exceed, those of human subjects on a variety of tasks, especially those related to image classification and pattern recognition. As one of the medical fields that is highly dependent on ancillary imaging tests, ophthalmology has been in a prime position to witness the application of deep learning algorithms that can help analyze the vast amount of data coming from those tests. In particular, glaucoma stands as one of the conditions where application of deep learning algorithms could potentially lead to better use of the vast amount of information coming from structural and functional tests evaluating the optic nerve and macula. The purpose of this article is to critically review recent applications of deep learning models in glaucoma, discussing their advantages but also focusing on the challenges inherent to the development of such models for screening, diagnosis and detection of progression. After a brief general overview of deep learning and how it compares to traditional machine learning classifiers, we discuss issues related to the training and validation of deep learning models and how they specifically apply to glaucoma. We then discuss specific scenarios where deep learning has been proposed for use in glaucoma, such as screening with fundus photography, and diagnosis and detection of glaucoma progression with optical coherence tomography and standard automated perimetry. Translational Relevance: Deep learning algorithms have the potential to significantly improve diagnostic capabilities in glaucoma, but their application in clinical practice requires careful validation, with consideration of the target population, the reference standards used to build the models, and potential sources of bias.

In clinical practice, glaucoma is usually diagnosed by a combined analysis of several clinical parameters, including risk factors, such as age and intraocular pressure; tests for evaluation of structural damage to the optic nerve; and visual function assessment with perimetry. Among tests for structural evaluation,^1,2 spectral-domain optical coherence tomography (SD-OCT) is the most commonly used one, providing objective quantification of damage to the optic nerve head and retinal nerve fiber layer (RNFL).³ To provide quantitative RNFL thickness measurements, conventional SD-OCT software applies segmentation algorithms to delineate the RNFL and extract its thickness. Although SD-OCT RNFL thickness measurements can be generally accurate for diagnosing glaucoma,^3,4 they may fail in the presence of segmentation errors.^5,6 Such errors have been reported in 20% to more than 40% of scans⁵ and significantly affect the diagnostic accuracy of SD-OCT in glaucoma.^5,6